Using Machine learning to enable feedback controlled manufacture of self-assembled patterned materials

使用机器学习实现自组装图案材料的反馈控制制造

• 批准号: EP/T004533/1
• 负责人: Stephen Ebbens
• 金额: $ 32.23万
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT004533%2F1
• 关键词: Using; Machine; learning; enable; feedback

Achieving control over materials' structure at small length scales is a critical requirement to realise new technologies with improved performance in areas of energy generation, energy storage and healthcare. To give some examples, solar cells, batteries and sensors for medical diagnostics can all benefit from nano and micron scale structuring. An attractive way to produce such structures is via self-assembly, where materials arrange themselves into well-defined regular patterns. One way to access this behaviour is by drying a suspension or solution containing the material of interest. For example, if a suspension of particles in a liquid is spread onto a solid support and left to dry, under some conditions very regular, repeating "crystalline" arrangements will result. These arrangements possess optical properties that can be used for sensors, and serve as templates to make efficient electrodes for batteries and solar cell materials. Other examples of useful structure formation processes in drying solutions include complex networks that form from multi-component mixtures, and regular crystalline structures, both of which can enhance the performance of solar cells if present at certain specific length scales. However, despite its simplicity and potential applications, at present self-assembly in drying solutions is mainly used as a research method to produce small quantities of material, and is not viewed as a routine manufacturing method. We believe that this is because self-assembly is highly sensitive to many parameters, which hampers reproducibility and requires time consuming optimisation to produce a given material, making it un-attractive for large scale manufacturing. Here, we plan to investigate if adding an automated control system to continuous self-assembly/solution structure based processes can overcome these obstacles. To implement the control system, we will make microscopic observations during self-assembly and use algorithms to adjust the manufacturing instruments parameters based on this feedback. This method will be used both to rapidly identify the parameters required to produce the ideal structure for a particular application, and also to maintain high quality uniform structure production during continuous manufacture of large amounts of material. Despite feedback being well established as an effective way to control manufacturing processes in other sectors, this method has not yet been deployed for self-assembly due to difficulties in observing the structure forming process, and the challenges of implementing the required algorithms to beneficially adjust parameters. Here we will use advances in real time monitoring of drying films, together with expertise in "machine learning" computer methods that are able to build models for complex behaviour, to overcome these challenges.

在少量尺度上实现对材料结构的控制是实现新技术，在能源产生，能源存储和医疗保健领域的性能提高的新技术。为了提供一些例子，用于医疗诊断的太阳能电池，电池和传感器都可以从纳米和微米尺度结构中受益。生产此类结构的一种有吸引力的方法是通过自组装，其中材料将自己整合到定义明确的常规图案中。访问此行为的一种方法是干燥悬浮液或溶液中包含感兴趣的材料。例如，如果液体中颗粒的悬浮液被扩散到固体支撑上，并在某些条件下散发出干燥，则会导致重复的“结晶”排列。这些布置具有可用于传感器的光学特性，并用作模板，以制造电池和太阳能电池材料的有效电极。干燥溶液中有用的结构形成过程的其他示例包括由多组分混合物和常规晶体结构形成的复杂网络，如果在某些特定的长度尺度上存在，则可以增强太阳能电池的性能。但是，尽管具有简单性和潜在应用，但目前在干燥溶液中自组装主要被用作生产少量材料的研究方法，并且不被视为常规制造方法。我们认为这是因为自组装对许多参数高度敏感，这会阻碍可重复性，并且需要耗时优化才能产生给定的材料，从而使其对大规模制造而没有吸引力。在这里，我们计划调查是否将自动控制系统添加到连续的自组装/基于解决方案结构的过程中可以克服这些障碍。为了实施控制系统，我们将在自组装过程中进行微观观察，并使用算法根据此反馈来调整制造工具参数。该方法将既可以用来快速识别为特定应用产生理想结构所需的参数，并在连续生产大量材料期间保持高质量均匀的结构产生。尽管反馈被很好地确定为控制其他领域的制造过程的有效方法，但由于在观察结构形成过程的困难以及实施所需算法以有益地调整参数的挑战，因此尚未将这种方法部署进行自组装。在这里，我们将实时监视干燥膜的进步，以及能够为复杂行为构建模型的“机器学习”计算机方法的专业知识，以克服这些挑战。